Spring 2019 Advanced Reporting
Rob Wells
1/4/2019
Inside Twitter: Data Analysis for Journalists
Jour 405v, Jour 5003, Spring 2019
Rob Wells, Ph.D.
University of Arkansas
School of Journalism and Strategic Media
@rwells1961
Course Goal: Students will learn the latest data journalism techniques that drive modern newsrooms and public relations / advertising offices. The class will extract and analyze Twitter data with the goal of producing an interactive multimedia presentation.
Course Description: This course will teach students how to code in programs such as R and SQL and how these powerful tools are used in modern news reporting. Quality reporting in newsrooms requires a solid foundation of data analysis. The data skills taught in this class are in high demand in newsrooms and corporations.
Required Text: Machlis, Sharon. Practical R for Mass Communications and Journalism. Chapman & Hall/CRC The R Series. 2018. ISBN 9781138726918 https://www.amazon.com/gp/search?keywords=9781138726918
Upon completion of this course, you will celebrate like this:
End of Intro
Week #1:Introduction and R Basics
Agenda: –Email to students.
–Intro R and R Studio. Open program. https://docs.google.com/presentation/d/1zICxR7qDM3RQ2Nxi5CqHlM3H8I7qoVkNtqcNcnbbDCw/edit#slide=id.p
–Load tutorial > Download this file and open it in R Studio: “CNTL” + click for a New Tab
Introduction-to-R-January-2019.R
–R interface explained.
There are four main windows:
- Script writing, R Markdown, Table Viewer: Upper Left
- Environment - data loaded in R: Upper Right
- Console - write commands in R: Lower Left
- File Manager and Html table viewer: Bottom Right
–Show basic R skills.
–Loading software. Tidyverse
Rio
–Workflow. Basic File Exploration 12-25-18.R
–Conventions in coding.
–Practice with Twitter dataset.
–nrow ncol
–rename columns. create columns
–Data Types and R
Machlis: 2.4.2 Data types you’re likely to use often 
–converting character strings into numeric see Downloading Data 12-24-18.R
Reading:
–Machlis. Chapter 1 & 2.
–Beginner’s guide to R: https://www.computerworld.com/article/2497143/business-intelligence/business-intelligence-beginner-s-guide-to-r-introduction.html
**Notes:** –Data Wrangling-Text Mining in Twitter. –See entire scraping sequence. Extract from Twitter. –Basic descriptive statistics –Chart –Export Static –Story
Resources: RStudio Navigation Tricks You Might’ve Missed https://rviews.rstudio.com/2016/11/11/easy-tricks-you-mightve-missed/
How Do I? https://smach.github.io/R4JournalismBook/HowDoI.html
Functions https://smach.github.io/R4JournalismBook/functions.html
Packages https://smach.github.io/R4JournalismBook/packages.html
End of Week 1
Week #2: Basic Data Viz. Use R instead of Excel. Loading and basic file management
Agenda: –Use R instead of Excel: Andrew Ba Tran
Excellent Tutorial Spelling out Excel and Comparable Commands in R
https://trendct.org/2015/06/12/r-for-beginners-how-to-transition-from-excel-to-r/
Basic data work- head to http://bit.ly/excel_and_r
–Basic data visualization Basic Data Visualization 12-26-18.R Basic-Chart-January-2018.R
–Export data Write Export output this file to a CSV or Excel write.csv or write.excel write.csv(AR2016_SMALL,“AR2016_SMALL.csv”)
–More on Data Types and R
Downloading Data 12-24-18.R
Exercise Median Income For City.R Basic crime rate in R exercise.txt
Notes –Loading and basic file management
Bringing in data
Data Frames
Extracting interesting details
Cleaning the data
Reshaping the format
Manipulating the data
Exporting 
Loading data RSQlite - read data from a database xlsx - read in Excel spreadsheets
Manipulating data dplyr - fast data work stringr - work with strings
lubridate - works with timestamps
Visualizing data ggplot2 - pretty charts and maps htmlwidgets - build web visualization interactives plotly - exporting charts online
Spatial data maptools - work with shapefiles
Math –Summary Statistics
.summary(Crime)
mean(x) Calculate the mean, or average, for variable x. median(x) Calculate the median. max(x) Find the maximum value. min(x) Find the minimum value. sum(x) Add all the values together. n() Count the number of records. Here there isn’t a variable in the brackets of the function, because the number of records applies to all variables. n_distinct(x) Count the number of unique values in variable x. percent_change <- function(first_number, second_number) { pc <- (second_number-first_number)/first_number*100 return(pc) }
percent_change(100,150) [1] 50 This is what’s happening in the code above: * percent_change is the name of the function, and assigned to it is the function function() * Two variables are necessary to be passed to this function, first_number and second_number * A new object pc is created using some math calculating percent change from the two variables passed to it * the function return() assigns the result of the math to percent_change from the first line Build enough functions and you can save them as your own package.
Data Management mutate Create new column(s) in the data, or change existing column(s). rename Rename column(s).
bind_rows Merge two data frames into one, combining data from columns with the same name.
Reading:
–Machlis. Chapter 3 & 4. Cohen, “Numbers in the Newsroom,” common mistakes. –Andrew Ba Tran
Resources
–All Cheat Sheets https://www.rstudio.com/resources/cheatsheets/
End of Week 2
Week #3: R Twitter Data. Data Types. Scripts
Agenda –Twitter Metadata
–Filters, Grouping, Sorting
–Add a column with a math conversion
Example: Total column shows winter snowfall in inches. To add a column showing totals in Meters, you can use this format:
.snowdata\(Meters <- snowdata\)Total * 0.0254
–Test - Basic R functions
–Tidyverse
–Setting up an R Workflow http://learn.r-journalism.com/en/publishing/workflow/r-projects/
Notes
–DPLYR Five basic verbs • filter() • select() • arrange() • mutate() • summarize() plus group_by() 
–Pipes pipe %>% CMD + Shift + M
–Presentation from Bob Rudis on Writing Readable Code with Pipes, delivered at the rstudio::conf 2017. https://www.rstudio.com/resources/videos/writing-readable-code-with-pipes/ It’s a good review of what Andrew discussed in the video, about how pipes ask as a way of chaining commands. He broke the functionality down in this fashion: object %>% operation() —> result
Reading Machlis Chs. 5 & 6. –Twitter analysis of Trump Tweets http://varianceexplained.org/r/trump-tweets/
Seth C. Lewis, et al. “Big Data and Journalism: Epistemology, Expertise, Economics and Ethics,” Digital Journalism, 2015
Materials from Andrew Ba Tran, Washington Post
Resources:
Exercises
Week #4: Using R to build basic graphs and charts
Agenda
–GGplot
–Describe Assignment #1: Static Graphic
–Conventions in coding
–R Markdown
Notes 
If you’re using ggplot: plus it! For everything else: pipe it!
So geom_point() geom_bar() geom_boxplot()
Resources
Reading
Machlis Chs. 7 & 8.
Samantha Sunne, “The Challenges and Possible Pitfalls of Data Journalism, and How You Can Avoid Them,” American Press Institute, 2016 Materials from Andrew Ba Tran, Washington Post
End of Week 5
| #Week #5: Using R to build basic graphs and charts |
| Agenda –Assignment #1 due: Static Graphic |
| –Themes for data viz library(ggthemes) |
| Terminology |
| **Resources* Grammar of Graphics http://vita.had.co.nz/papers/layered-grammar.html |
| –Graphing GGplot 12-28.R Exercises from Machlis Ch. 9. Facets |
| Notes –The pie chart focuses the reader on large percentages, and encourages the reader to think of the total –The stacked bar plot provides the same information, but makes it easier to accurately determine at a glance how large each group is out of the whole. –This bar chart splits the categories horizontally, and draws attention to how the family members are ordered. It encourages the reader to think about the distribution rather than disconnected categories, and gives a better sense of sense of scale. |
| Reading: Machlis Chs. 9 & 10 Albert Cairo, “The Functional Art,” Principles of Data Visualization. |
| Exercises –Create R Markdown document, export to PDF, HTML –Class Exercise - Graphing and Grouping Data Viz Exercise 2 From Ch 9 |
| End of Week 5 |
Week #6: GitHub
Agenda –Create a GitHub account. https://github.com/
–Follow this tutorial https://guides.github.com/activities/hello-world/
This class is intended to teach you modern workflow techniques for coding. A centerpiece of that workflow is GitHub. This is a website with a system that allows you to collaborate with other programmers on coding projects. It manages versions of software code and is a very popular with the tech elite.
Your GitHub account, which is public, represents an important professional image. Prospective employers and collaborators will look at your GitHub account.
–Andrew Ba Tran Tutorials on GitHub Git and Github Pages http://learn.r-journalism.com/en/git/
Installing Git https://journalismcourses.org/courses/RC0818/installing_git.pdf
GIT https://journalismcourses.org/courses/RC0818/git.pdf
Connecting to Github https://journalismcourses.org/courses/RC0818/github.pdf http://learn.r-journalism.com/en/git/github/github/
Best Practices for Github http://learn.r-journalism.com/en/git/github_pages/github-pages/
Terminology
Commit
Branch
Pull Request
Fork
Resources on GitHub
–GitHub flow
https://guides.github.com/introduction/flow/
–GitHub Guides
https://guides.github.com/
Reading:
Machlis Chs. 11 & 12
Exercises
Pair up.
Team 1 takes this code. Make XXX changes.
Team 2 forks the code. Makes XXX changes.
Pull & Commit
Workbooks, Markdown
Week #7: Twitter
Agenda
–Analyzing Tweets from public officials. DATASET TK TK
–Study Twitter meta data
https://developer.twitter.com/en/docs/tutorials https://twittercommunity.com/ https://developer.twitter.com/en/docs
–Register as Twitter Developer https://developer.twitter.com/en/account/get-started
–Test on GitHub
–Data Wrangling http://learn.r-journalism.com/en/wrangling/
http://learn.r-journalism.com/en/wrangling/dplyr/dplyr/
https://github.com/r-journalism/learn-chapter-3/blob/master/dplyr/pipes-dplyr.R
Reading Machlis Chs. 13 & 14.
Twitter meta data
Resources:
–For analysis library(dplyr) –For working with dates library(lubridate)
Exercises
Week #8: R Markdown
Agenda –R Markdown, Desktop Publishing –Andrew Ba Tran - Week 5 Publishing http://learn.r-journalism.com/en/publishing/ –R Markdown http://learn.r-journalism.com/en/publishing/rmarkdown/rmarkdown/ –More R Markdown http://learn.r-journalism.com/en/publishing/more_rmarkdown/more-rmarkdown/
–Rendering html as an output in GitHub
https://rmarkdown.rstudio.com/lesson-9.html https://github.com/rstudio/cheatsheets/raw/master/rmarkdown-2.0.pdf
–R Markdown Formatting
Sizing images: <.img src=“drawing.jpg” alt=“drawing” width=“200”/>
(Note: Remove the period before “img”) https://rpubs.com/RatherBit/90926
Terminology
–Render
–Html
–Markdown
Notes R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00Including Plots
You can also embed plots, for example:
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.
Reading
Machlis Chs. 15 & 16
Resources:
Exercises
Line breaks: Use HTML tags. Adding
will give a single line break – option for when two-space indentation is ignored.
Week #9: Text Mining
Agenda –Basic text mining techniques
–Lubridate
–Due: Assignment #2. Visualization of Twitter data
Reading Machlis Chs. 17 & 18
Resources:
Exercises
SPRING BREAK – MAKE GOOD LIFE CHOICES
.
Week #10: Mapping in R
You will make this in the class
Agenda –Produce a basic interactive map in R, post on WordPress
–Andrew Ba Tran - Week 4 Mapping http://learn.r-journalism.com/en/mapping/
Reading “Connecting the Dots” by Jacob Harris (2015) and discuss how people should or should not be represented through news visualizations.
What is code? http://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/
Resources: –Visual Narrative Tricks by Albert Cairo https://www.youtube.com/watch?v=TSGaueL4Ggk
Exercises –Maps in R 12-28-18.R
Week #11: Mapping in R
Agenda –Produce a basic interactive map in R, post on WordPress
You will make this in the class
Reading Bad data visualizations. Data Translation.
The Journalist as Programmer: A Case Study of The New York Times Interactive News Technology Department http://isoj.org/wp-content/uploads/2016/10/ISOJ_Journal_V2_N1_2012_Spring.pdf
Resources:
Exercises
Week #12: Web Scraping in R
Agenda –Web Scraping in R
–Application Programming Interface - API
Twitter historical API https://developer.twitter.com/en/docs/tutorials/choosing-historical-api
–See 4.8 in Machlis: ‘Scrape’ data from Web pages with the rvest package and SelectorGadget browser extension or JavaScript bookmarklet. SelectorGadget helps you discover the CSS elements of data you want to copy that are on an HTML page; then rvest uses R to find and save that data. This is not a technique for raw beginners, but once you’ve got some R experience under your belt, you may want to come back and re-visit this. I have some instructions and a video on how to do this at http://bit.ly/Rscraping. RStudio has a webinar available on demand as well, at https://www.rstudio.com/resources/webinars/extracting-data-from-the-web-part-2/ .
–Assignment #3. Interactive Map
Reading Julia Angwin, Terry Parris Jr., Surya Mattu. “Breaking the Black Box: What Facebook Knows About You,” ProPublica, 2016;
Nicholas Diakopolous, “Algorithmic Accountability,” Digital Journalism, 2014.
MA Students –Sign up for a Census API key: https://api.census.gov/data/key_signup.html
Resources:
Exercises –MA Students, Census API assignment
Week #13: Web Scraping in R
Agenda – Web Scraping in R
Reading –Twitter analysis of Trump Tweets http://varianceexplained.org/r/trump-tweets/
Amy Webb future of journalism trends https://futuretodayinstitute.wetransfer.com/downloads/0e84e883e140bafe9a3436a6464032be20171003123607/ecda17 Resources: Exercises ——————————————————————–
Week #14: Sentiment Analysis
Agenda –Sentiment Analysis
Reading –Sentiment analysis http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm –Saif Mohammad research on sentiment analysis http://saifmohammad.com/index.html
Resources:
Exercises
Week #15:
Agenda Reading Resources: Exercises
Download R and RStudio
This program is loaded on all of the computers in Kimpel 146. You can load it on your laptop as well. It is not large and doesn’t tax the memory a lot. R runs on Windows, Mac and Linux, but this course uses the Mac version. If you use Windows on your personal laptop, the instructor is not responsible for any variations in the lessons and instructions.
First, download the most recent version of R at https://www.r-project.org/, . Look for the link to download R. Click that download option and you should be taken to CRAN, the Comprehensive R Archive Network, and a list of CRAN servers, called mirrors, around the world. Pick a server and choose the precompiled binary distribution for your operating system. Once the file finishes downloading, install it like any other software program – run the .exe for Windows or .pkg for Mac.
Accept all of the default settings for Mac.
Second, install RStudio, an excellent user interface that helps you manage and create R code. Download the open source edition of R Studio desktop and follow the prompts to install it. http://www.rstudio.com/products/rstudio/download/
More information: http://www.machlis.com/R4Journalists/download-r-and-rstudio.html
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